15 citations · 36 across the 12 of their papers we have counts for
12 papers
Adversarial Prompt Evaluation: Systematic Benchmarking of Guardrails Against Prompt Input Attacks on LLMs
Giulio Zizzo, Giandomenico Cornacchia, Kieran Fraser +7
As large language models (LLMs) become integrated into everyday applications, ensuring their robustness and security is increasingly critical. In particular, LLMs can be manipulate…
Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation
Tomas Bueno Momcilovic, Beat Buesser, Giulio Zizzo +2
Despite the impressive adaptability of large language models (LLMs), challenges remain in ensuring their security, transparency, and interpretability. Given their susceptibility to…
Knowledge-Augmented Reasoning for EUAIA Compliance and Adversarial Robustness of LLMs
Tomas Bueno Momcilovic, Dian Balta, Beat Buesser +2
The EU AI Act (EUAIA) introduces requirements for AI systems which intersect with the processes required to establish adversarial robustness. However, given the ambiguous language…
Towards Assuring EU AI Act Compliance and Adversarial Robustness of LLMs
Tomas Bueno Momcilovic, Beat Buesser, Giulio Zizzo +2
Large language models are prone to misuse and vulnerable to security threats, raising significant safety and security concerns. The European Union's Artificial Intelligence Act see…
Developing Assurance Cases for Adversarial Robustness and Regulatory Compliance in LLMs
Tomas Bueno Momcilovic, Dian Balta, Beat Buesser +2
This paper presents an approach to developing assurance cases for adversarial robustness and regulatory compliance in large language models (LLMs). Focusing on both natural and cod…
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI
Ambrish Rawat, Stefan Schoepf, Giulio Zizzo +10
As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a f…
Boundary Adversarial Examples Against Adversarial Overfitting
Muhammad Zaid Hameed, Beat Buesser
Standard adversarial training approaches suffer from robust overfitting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this p…
Automated Robustness with Adversarial Training as a Post-Processing Step
Ambrish Rawat, Mathieu Sinn, Beat Buesser
Adversarial training is a computationally expensive task and hence searching for neural network architectures with robustness as the criterion can be challenging. As a step towards…
FAT: Federated Adversarial Training
Giulio Zizzo, Ambrish Rawat, Mathieu Sinn +1
Federated learning (FL) is one of the most important paradigms addressing privacy and data governance issues in machine learning (ML). Adversarial training has emerged, so far, as…
How can AI Automate End-to-End Data Science?
Charu Aggarwal, Djallel Bouneffouf, Horst Samulowitz +9
Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it. This makes data science time consuming and restricted to experts with the r…
Adversarial Robustness Toolbox v1.0.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran +9
Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision…
Neural Feature Learning From Relational Database
Hoang Thanh Lam, Tran Ngoc Minh, Mathieu Sinn +2
Feature engineering is one of the most important but most tedious tasks in data science. This work studies automation of feature learning from relational database. We first prove t…